US8239535B2 - Network architecture with load balancing, fault tolerance and distributed querying - Google Patents
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
- H04L67/1004—Server selection for load balancing
- H04L67/1008—Server selection for load balancing based on parameters of servers, e.g. available memory or workload
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/06—Generation of reports
- H04L43/065—Generation of reports related to network devices
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0805—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability
- H04L43/0817—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability by checking functioning
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
- H04L67/1004—Server selection for load balancing
- H04L67/1012—Server selection for load balancing based on compliance of requirements or conditions with available server resources
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
- H04L67/1034—Reaction to server failures by a load balancer
Definitions
- This invention relates generally to system for responding to data queries. For example for web analytics data. More particularly, the present invention relates a system and method that reduce the time for response to multiple queries by load balancing over computing devices while also providing fault tolerance.
- Web analytics basically refers to the analysis of data created by website usage. For instance, web analytics could be used to mine visitor traffic data. A variety of visitor traffic data is measured such as what web browser is being used, what links on a given web page were selected, whether a product was purchase, etc. There are number of web analytics tools presently available such as Site Catalyst version 11 from Omniture of Orem, Utah.
- the present invention is a network architecture with load balancing, fault tolerance and distributed querying.
- the system of the present invention preferably comprises a plurality of front-end servers, a plurality of back-end servers, and a database.
- the front-end servers are coupled to a network to receive data requests from client devices.
- the front-end servers are each coupled to the plurality of back-end servers.
- the front-end servers handle data requests at a macro level and divide the request into sub-requests that are sent to the plurality of back-end servers.
- the back-end servers are coupled to the database to retrieve data. Each data request is distributed across the plurality of back-end servers according to workload.
- the front-end servers are fault tolerant in that they can respond to a request for data without all of the back-end servers being responsive or providing data.
- the present invention also includes a plurality of methods including: a method for distributed querying, a method for loading data sets from a database, and a method for responding to a query that provides fault tolerance.
- FIG. 1 is a block diagram of a system for operating, web analytics tools of the present invention.
- FIG. 2 is block diagram of a preferred embodiment of a network according to the present invention.
- FIG. 3 is graphical representation of data in a database including a data set and slices of the data set.
- FIG. 4 is a flowchart of one embodiment of a method for accessing a web analytics system and running queries according to the present invention.
- FIG. 5 is a flowchart of one embodiment of a method for loading a data set according to the present invention.
- FIG. 6 is a flowchart of one embodiment of a method for responding to a query or request for data according to the present invention.
- FIG. 7 is graphical representation of load balancing across multiple back-end servers at different points in time for multiple visitors each having a data set including multiple slices.
- the present invention is a network architecture with load balancing, fault tolerance and distributed querying. While the present invention and its principles will now be described in the context of a network 102 used as part of a web analytics system, this is only by way of example. Those skilled in the art will recognize that the present invention may be used in any variety of general purpose databases to provide the same advantages as noted below.
- the present invention also relates to apparatus for performing the operations herein.
- This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer.
- a computer program may be stored in a computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus.
- FIG. 1 there is shown an example of an architecture for practicing the present invention according to one embodiment.
- One skilled in the art will recognize that the invention can be practiced using other embodiments that differ from the examples shown.
- Java client 101 runs on a personal computer for viewing and interacting with website usage reports. Client 101 sends reports to display 107 (or other output device) for output to the user.
- Network 102 is a centralized network for handling and responding to client requests for data on website usage.
- the user interface is implemented using a known environment such as Macromedia Flex, Java, DHTML, or any combination thereof.
- Java client 101 sends query 103 to network 102 , specifying which reports are requested, and optionally specifying one or more filters for the reports.
- query 103 is in XML format.
- network 102 returns hashed data 104 that contains an encoded representation of the report data.
- the hashed data 104 may specify, in hash coded terms, the number of visitors that were using a specific web browser and that visited the website within a specified time period. This hashed data 104 is received by client 101 .
- Client 101 stores, in local cache 109 , a list of previously received and decoded hash codes, so that it can correctly interpret a hash code that it has encountered previously.
- local cache 109 is cleared at the end of a session, so that only those codes previously received in the same session are stored in cache 109 .
- local cache 109 is implemented in a more persistent or less persistent fashion, depending on user needs.
- client 101 Upon receiving hashed data 104 , client 101 consults cache 109 ; if cache 109 contains the hash code(s) in data 104 (in other words, if client 101 has previously received data containing the same hash code), client 101 can interpret the meaning of the hash-coded data without any further communication with network 102 . If hash code(s) from data 104 is/are not present in cache 109 , client 101 sends hash query 105 to network 102 ; network 102 responds by sending hash translation 106 to client 101 . Hash translation 106 provides client 101 with the meaning of hash terms (for example, specifying that hash term #299 signifies a user using Internet Explorer 6.0). In one embodiment, client 101 stores this meaning in cache 109 for future use.
- the present invention can employ a method for using hash values as disclosed in related U.S. patent application Ser. No. 11/313,443, filed on Dec. 20, 2005, entitled METHOD FOR COMMUNICATION BETWEEN COMPUTING DEVICES USING CODED VALUES.
- client 101 Once client 101 has received sufficient data to generate a report, it sends report 108 to display 107 for output to the user. In one embodiment, if some hash meanings have not yet been received, client 101 still sends report 108 , and report 108 states that certain hash terms are unknown. In another embodiment, client 101 displays an error message and/or waits until more complete hash meaning data is available.
- the user can interact with the displayed report 108 via user input device 110 such as a mouse, a keyboard, or the like.
- user input device 110 such as a mouse, a keyboard, or the like.
- the user can click on areas within report 108 ; when the user clicks on an area that can be interpreted as a filter, client 101 generates and sends a new query 103 containing the new report filter criteria.
- the above process then repeats, and an updated report 108 is sent to display 107 .
- Network 102 includes any number of front-end web servers 201 a - n that receive queries 103 , 105 from client 101 , and any number of back-end servers 202 a - n that obtain data from storage, analyze the obtained data, and send report data back to client 101 .
- Back-end servers 202 a - n send an appropriate data set to client 101 based on the filter request. For example, if a filter request specifies that the user is only interested in visitors that used a particular web browser, back-end servers 202 a - n remove the data that does not match the specified criterion, and only forward to client 101 the data that does match.
- back-end servers 202 a - n are applying a movable filter bar to the data set, maintaining consistency in the views into the data while changing the size of the data set according to the filter request.
- Database 203 contains website visitation data, which in one embodiment is stored in a binary format stored in some storage medium such as a hard drive.
- the website visitation data is broken up into files, or “bricks”, to facilitate extraction of portions of the data.
- servers 202 a - n extract data from database 203 , they are provided with specific bricks that match the criteria.
- back-end servers 202 a - n extract data from database 203 that contains web visitation logs and/or statistics.
- servers 202 a - n obtain data from database 203 that represents a snapshot of website visitation over a specified time period.
- Servers 202 a - n then store this website visitation data in temporary local storage (such as random access memory), using for example a binary format that is encoded according to a hash algorithm so as to minimize bandwidth usage.
- this binary format is identical to the format used in database 203 , so that no file format translation need be performed when servers 202 a - n extract data from database 203 .
- Servers 202 a - n and then apply filters as requested, and send the filtered data to client 101 .
- the back-end servers 202 a - n perform a new data extraction from database 203 .
- the step of data extraction may be performed using any combination of data ranges for the data in the database 203 and is not restricted to splitting the data by date range. In alternate embodiments, any other values could be used to partition the data.
- the front-end servers 201 a - n are coupled 204 for communication with the back-end servers 202 a - n .
- Each front-end server 201 a - n can be connected 204 to the other back-end servers 202 a - n as shown, or a variety of other network topologies may be used so long as messages can be broadcast among the front-end servers 201 a - n and the back-end servers 202 a - n.
- FIG. 3 illustrates how and where the data is stored and processed in order to respond to requests or queries from the client 101 .
- All the website visitation data 302 is stored in the database 203 .
- the website visitation data 302 can be for multiple clients 101 and includes all the data about web statistics for those sites.
- a data set 304 is extracted from all the data 302 in the database 203 for use by the client 101 .
- the data set 304 is preferably smaller in size than all the data 302 so that it can be effectively transmitted and manipulated. In an exemplary embodiment, the data set 1 MB in size.
- the data set 304 is advantageously created on a per client visit or client session basis.
- the data set 304 is further divided for processing by individual backend servers 202 a - n into data slices 306 .
- the data slices 306 are preferably each a complete uncompromised segment of the data set 304 that can be used to provide a statistically valid response to a query.
- the division of slices 306 across backend servers 202 a - n is described in more detail below with reference to FIGS. 5 and 7 , but is particularly advantageous because it provides improved speed in responding to queries since each slice can be processed in parallel.
- the distribution of slices over the backend servers 202 a - n provides a level of fault tolerance because if one more the backend servers 202 a - n do not provide their a response for their slice, the system, in particular the front-end servers 201 a - n are able to generated a response from those back-end servers 202 a - n that do respond and scale it so that it is representative of the entire data set as will be discussed below with reference to FIG. 6 .
- the process begins when a visitor or client 101 logs 402 onto the system. Upon log in, each visitor is assigned 404 a visitor identification number. This visitor identification number is used to assign the visitor/client 101 to a particular one of the plurality of front-end servers 210 a - n . It does not matter which front-end server 210 a - n is assigned to the visitor/client 101 ; and the front-end servers 210 a - n are preferably assigned to accomplish load balancing of requests across the plurality of front-end servers 210 a - n .
- the visitors with identification number 1 - 100 are assigned to front-end server 201 a
- visitors with identification number 200 - 300 are assigned to front-end server 201 b
- visitors with identification number 300 - 400 are assigned to front-end server 201 c
- front-end server 201 a - n there is a variety of other ways to distribute visitors over the front-end servers 201 a - n such as every nth visitor being assigned to front-end server 201 a , every n+1 visitor assigned to front-end server 201 b , etc.
- the step of logging in is optional and that in an alternate embodiment, a user need not log in, but rather clients 101 can just send data requests to one of the front-end servers 210 a - n ; and the front-end server 210 a - n that receives the request can respond to it.
- step 406 based on the ID number the visitor, the corresponding front-end servers 210 a - n is notified and communication is established between that front-end server 210 a - n and the visitor. Then the assigned front-end server 210 a - n divides the request into slices and sends them to the back-end servers 202 a - n to retrieve 408 the data set 304 and load it to the back-end servers 202 a - n , as will be described in more detail below with reference to FIG. 5 .
- One advantage of the present invention is that the data set is divided into slices and the slices are assigned to one or more back-end servers 202 a - n based on the availability of the back-end servers 202 a - n .
- the system continues to determine 416 if the network 102 has failed (either all the front-end servers 201 a - n or not operational or all the back-end servers 202 a - n are not operational) if so the process ends. If not the process returns to step 412 to continue to process additional requests from the visitor. If the front-end server 201 determined the visitor has logged out or there are has been a predetermined amount of time of inactivity by the visitor in step 414 , the front-end server 210 will clear 418 the data set 304 for the visitor from the back-end servers 202 a - n . This is particularly advantageous because it optimizes the use of the front-end servers 201 a - n and ensures that only in-use data is loaded on the back-end servers 202 a - n.
- the process for loading 408 the data set 304 to the back-end servers 202 a - n begins in step 502 with the front-end server 201 identifying the data set 304 corresponding to the visitor. It should be noted that the data is not associated with the visitor, but rather data that is need by a visitor remains loaded on the back-end servers 202 a - n such that if another visitor needs the data it remains loaded until a predetermined time of non-use has elapsed after which the backend servers 202 a - n are not required to keep the data loaded. Then the front-end sever 201 divides 504 the data set into a plurality of slices 306 .
- each of the slices 306 is a complete uncompromised segment of the data set 304 that can be used to provide a statistically valid response to a query.
- the front-end sever 201 selects 506 a slice 306 and broadcasts a request for one of the back-end severs 202 to retrieve the slice 306 .
- This request is preferably broadcast to all available back-end servers 202 such as with a UDP broadcast message.
- Each back-end server 202 a - n reports 508 its load and availability to the front-end server 201 .
- the load and availability of a particular back-end server 202 a - n can be based on a variety of factor including: the number of slices already being processed, the processing power/speed of the back-end server 202 , the communication/connection speed available to the back-end server 202 , and other processing characteristics as will understood to those of ordinary skill in the art.
- the front-end sever 201 selects 510 the back-end server 202 a - n with the lowest load and best availability and transfers control to the selected back-end server 202 . This selection is solely to achieve load balancing across the back-end servers 202 a - n .
- the selected back-end server 202 then loads 512 the identified slice 306 from the database 203 , and sends a confirmation message back to the front-end server 201 indicating the data slice 306 has been loaded.
- the selected back-end server 202 records 512 the percentage of the data set 304 that the slice 306 represents. Then the selected back-end server 202 transfers 516 control back to the front-end server 201 . Then the front-end server 201 determines 518 whether there are any more slices 306 of the data set 304 to be loaded to back-end server 202 a - n . If so the process returns to step 506 to repeat the process for the next data slice 306 .
- a key advantage of the present invention is that the process for loading data slices 306 to the back-end servers 202 a - n is masterless and done in an anarchistic way. In other words, maintenance of the data is masterless.
- one of the front-end servers 201 a - n temporarily acts as an overseer to divide the data set into slices and send a request to load the slice to which every back-end servers 202 a - n will accept the request.
- step 506 - 516 for given slices may be done in whole or part in parallel by broadcasting such requests between the front-end server 201 and the back-end servers 202 a - n before the remaining steps 512 - 516 complete.
- the front-end server 201 does not care which back-end server 202 a - n responds to the request to retrieve the slice 306 of the data set 304 , nor does it track which of the back-end servers 202 a - n responded and holds the data slice 306 .
- FIG. 7 an example of the load and how it is balanced over four back-end servers 202 a - 202 d at four different times T 1 -T 4 is shown.
- time T 0 (not shown) there are no visitors and no slices are loaded on any of the back-end servers 202 a - d .
- a visitor, V 1 logs onto the system and its data set is loaded.
- a time T 1 there are only three servers active, servers 202 a - c .
- the data set 304 for the first visitor (V 1 ) has three slices (S 1 -S 3 ).
- a first slice (V 1 S 1 ) is loaded on back-end server 202 a
- a second slice (V 1 S 2 ) is loaded on back-end server 202 b
- a third slice (V 1 S 3 ) is loaded on back-end server 202 c .
- a second visitor V 2 with a data set 304 having four data slices V 1 -V 4 is loaded onto the back-end servers 203 a - c .
- an additional server 202 d is added to the back-end, and a new visitor V 4 with a data set 304 having four slices S 1 -S 4 logs onto the system. Because the additional server 202 d is new and has a high availability, all four of the slices for visitor V 4 S 1 -V 4 S 4 are loaded on the new back end server 202 d as shown at time T 4 in FIG. 7 . Thus, those skilled in the art will understand how back-end servers 202 can easily be added when additional capacity is needed. Similarly, when a server 202 fails or need to be removed, it can respond to the front-end server 201 with no availability until there are no slices loaded on it then it may be removed. This increased the fault recovery of the system.
- FIG. 6 one embodiment of the method for processing a query is shown.
- the data set has already been loaded on the back-end servers 202 a - n as discussed above with reference to FIGS. 5 and 7 .
- the process begins with a query from a visitor received 602 at the front-end server 201 .
- the front-end server 201 assigned to communicate with the visitor according the visitor's number generates and sends 604 a broadcast query to all or a plurality of back end servers 202 .
- the query needs to broadcast to a plurality, but preferably as many as possible of the back-end servers 202 . Then each of the back-end servers 202 receives the query or request, and determines 606 if they have the data for the query. Then in step 608 , the back-end servers 202 that have the data for the query, perform the query on their portion of the data set 304 . Each of the back-end servers 202 that have data send the result data and what percentage of the entire data set 304 they have back to the front-end server 201 .
- the front-end server 201 determines if 100% of the data set has been returned by whatever responses it received from the back-end severs 202 . If so, the method proceeds to step 614 where it combines the result data received from each of the back-end servers 202 and sends it to the visitor, and the response to the query is complete.
- step 612 If in step 612 , it is determined that less than 100% of the data set 304 has been returned, the method continues to step 616 where the front-end server 201 combines the result data from the back-end servers 202 . Then the percentage of data from the data set 304 is determined 618 . This can be done by summing the percentages received from each back-end server 202 for this visitor for this request from step 610 . Then the front-end server 201 multiplies 620 the data from the query by the inverse of the percentage determined in step 618 . Then the multiplied values are sent 622 to the visitor as the result data for the query or request.
- the front-end server 201 is still able to create a response from the back-end servers 202 that do respond. This provides a significant advantage of fault tolerance in the event one of the back-end servers 202 fails or does not respond to the data request in a timely manner.
- the rules applied can be dynamic depending on the size of the data set 304 . For example, if the data set is large, a response by 50% of the slices may be sufficient, whereas if the data set is small, a higher percentage such as 80% of the slices must be present to send valid data back to the visitor.
- modules, routines, features, attributes, methodologies and other aspects of the present invention can be implemented as software, hardware, firmware or any combination of the three.
- a component an example of which is a module, of the present invention is implemented as software
- the component can be implemented as a standalone program, as part of a larger program, as a plurality of separate programs, as a statically or dynamically linked library, as a kernel loadable module, as a device driver, and/or in every and any other way known now or in the future to those of ordinary skill in the art of computer programming.
- the present invention is in no way limited to implementation in any specific programming language, or for any specific operating system or environment. Accordingly, the disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the present invention, which is set forth in the following claims.
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